"""simple docstring"""



import contextlib
from multiprocessing import Pool, RLock

from tqdm.auto import tqdm

from ..utils import experimental, logging


SCREAMING_SNAKE_CASE__:List[Any]									=			logging.get_logger(__name__)






class  snake_case__      :
 _snake_case       :					Optional[int]							  =						None



@experimental
def 	_lowerCamelCase(	a   ,    a   ,    a   ,    a   ,    a   ,    a   ,    a				):
       if ParallelBackendConfig.backend_name is None:
              return _map_with_multiprocessing_pool(
                  a   ,    a   ,    a   ,    a   ,    a   ,    a   ,    a				)

       return _map_with_joblib(a   ,    a   ,    a   ,    a   ,    a   ,    a   ,    a				)



def 	_lowerCamelCase(	a   ,    a   ,    a   ,    a   ,    a   ,    a   ,    a				):
       __a       =    num_proc if num_proc <= len(a				) else len(a				)
       __a       =    []  # We organize the splits ourselve (contiguous splits)
       for index in range(a				):
              __a       =    len(a				) // num_proc
              __a       =    len(a				) % num_proc
              __a       =    div * index + min(a   ,    a				)
              __a       =    start + div + (1 if index < mod else 0)
              split_kwds.append((function, iterable[start:end], types, index, disable_tqdm, desc)				)

       if len(a				) != sum(len(i[1]				) for i in split_kwds				):
              raise ValueError(
                  F"Error dividing inputs iterable among processes. "
                  F"Total number of objects {len(a				)}, "
                  F"length: {sum(len(i[1]				) for i in split_kwds				)}"				)

       logger.info(
           F"Spawning {num_proc} processes for {len(a				)} objects in slices of {[len(i[1]				) for i in split_kwds]}"				)
       __a ,   __a       =    None, None
       if not disable_tqdm:
              __a ,   __a       =    (RLock(),), tqdm.set_lock
       with Pool(a   ,    initargs=a   ,    initializer=a				) as pool:
              __a       =    pool.map(a   ,    a				)
       logger.info(F"Finished {num_proc} processes"				)
       __a       =    [obj for proc_res in mapped for obj in proc_res]
       logger.info(F"Unpacked {len(a				)} objects"				)

       return mapped



def 	_lowerCamelCase(	a   ,    a   ,    a   ,    a   ,    a   ,    a   ,    a				):
       # progress bar is not yet supported for _map_with_joblib, because tqdm couldn't accurately be applied to joblib,
       # and it requires monkey-patching joblib internal classes which is subject to change
       import joblib

       with joblib.parallel_backend(ParallelBackendConfig.backend_name   ,    n_jobs=a				):
              return joblib.Parallel()(
                  joblib.delayed(a				)((function, obj, types, None, True, None)				) for obj in iterable				)




@experimental
@contextlib.contextmanager
def 	_lowerCamelCase(	a				):
       __a       =    backend_name

       if backend_name == "spark":
              from joblibspark import register_spark

              register_spark()

              # TODO: call create_cache_and_write_probe if "download" in steps
              # TODO: raise NotImplementedError when Dataset.map etc is called

       try:
              yield
       finally:
              __a       =    None

"""simple docstring"""



from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_sentencepiece_available,
    is_tokenizers_available,
    is_torch_available,
)


SCREAMING_SNAKE_CASE__:Optional[Any]									=			{"""configuration_reformer""": ["""REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP""", """ReformerConfig"""]}

try:
   if not is_sentencepiece_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE__:Optional[Any]									=			["""ReformerTokenizer"""]

try:
   if not is_tokenizers_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE__:List[str]									=			["""ReformerTokenizerFast"""]

try:
   if not is_torch_available():
      raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
   pass
else:
   SCREAMING_SNAKE_CASE__:int									=			[
       """REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST""",
       """ReformerAttention""",
       """ReformerForMaskedLM""",
       """ReformerForQuestionAnswering""",
       """ReformerForSequenceClassification""",
       """ReformerLayer""",
       """ReformerModel""",
       """ReformerModelWithLMHead""",
       """ReformerPreTrainedModel""",
   ]


if TYPE_CHECKING:
   from .configuration_reformer import REFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, ReformerConfig

   try:
      if not is_sentencepiece_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_reformer import ReformerTokenizer

   try:
      if not is_tokenizers_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .tokenization_reformer_fast import ReformerTokenizerFast

   try:
      if not is_torch_available():
         raise OptionalDependencyNotAvailable()
   except OptionalDependencyNotAvailable:
      pass
   else:
      from .modeling_reformer import (
          REFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
          ReformerAttention,
          ReformerForMaskedLM,
          ReformerForQuestionAnswering,
          ReformerForSequenceClassification,
          ReformerLayer,
          ReformerModel,
          ReformerModelWithLMHead,
          ReformerPreTrainedModel,
      )

else:
   import sys

   SCREAMING_SNAKE_CASE__:int									=			_LazyModule(__name__, globals()["""__file__"""], _import_structure, module_spec=__spec__)
